Growth performance, carcass traits and meat quality of heavy lambs reared in a warm or cold environment during winter
Bibliographic record
Abstract
The goal of this study was to evaluate the impact of winter rearing environment on the growth performance and meat quality of heavy lambs. Half of sixty-four Dorset lambs (32 males and 32 females) were raised in each of two different environments: warm and cold with average temperature of 10.9 ± 0.7 °C and -2.0 ± 5.2 °C, respectively. The lambs were slaughtered at live weights of 41-45 kg for females and 46-50 kg for males. Cold environment had no adverse effect on either growth performance or carcass quality. The rate of longissimus dorsi muscle deposition (P = 0.049) and its depth at slaughter (P = 0.027) were rather greater in lambs reared in the cold environment and a higher proportion of oxido-glycolytic fibres (P = 0.047) was also observed in this muscle. Rearing environment had only a minor effect on the organoleptic qualities, with the cold environment promoting juiciness of the meat (P = 0.043). Therefore, cold environment rearing such as used in this study represents an economic advantage for lamb producers by reducing the costs associated with the construction of insulated barns, while maintaining growth performance, as well as carcass and meat quality. Key words: Lamb, rearing environment, temperature, growth, carcass, meat quality
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".